14 citations · 16 across the 16 of their papers we have counts for
8 papers · 1 filter
PolarZero: A Reinforcement Learning Approach for Low-Complexity Polarization Kernel Design
Yi-Ting Hong, Stefano Rini, Luca Barletta
Polar codes with large kernels can achieve improved error exponents but are challenging to design with low decoding complexity. This work investigates kernel construction under rec…
STSM-FiLM: A FiLM-Conditioned Neural Architecture for Time-Scale Modification of Speech
Dyah A. M. G. Wisnu, Ryandhimas E. Zezario, Stefano Rini +4
Time-Scale Modification (TSM) of speech aims to alter the playback rate of audio without changing its pitch. While classical methods like Waveform Similarity-based Overlap-Add (WSO…
Improving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddings
Dyah A. M. G. Wisnu, Ryandhimas E. Zezario, Stefano Rini +2
We present a system for automatic multi-axis perceptual quality prediction of generative audio, developed for Track 2 of the AudioMOS Challenge 2025. The task is to predict four Au…
DOA Estimation via Optimal Weighted Low-Rank Matrix Completion
Saeed Razavikia, Mohammad Bokaei, Arash Amini +2
This paper presents a novel method for estimating the direction of arrival (DOA) for a non-uniform and sparse linear sensor array using the weighted lifted structure low-rank matri…
PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
Liangyan Li, Yangyi Liu, Yimo Ning +2
Federated Learning (FL) has emerged as a powerful paradigm for leveraging diverse datasets from multiple sources while preserving data privacy by avoiding centralized storage. Howe…
Reinforcement Learning-Aided Design of Efficient Polarization Kernels
Yi-Ting Hong, Stefano Rini, Luca Barletta
Polar codes with large kernels achieve optimal error exponents but are difficult to construct when low decoding complexity is also required. We address this challenge under recursi…
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
Ori Peleg, Natalie Lang, Dan Ben Ami +3
Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proce…
The Query/Hit Model for Sequential Hypothesis Testing
Mahshad Shariatnasab, Stefano Rini, Farhad Shirani +1
This work introduces the Query/Hit (Q/H) learning model. The setup consists of two agents. One agent, Alice, has access to a streaming source, while the other, Bob, does not have d…